This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
:::info Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es).
:::
Table of Links
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Appendix A. Hyperparameters used during training.
This appendix contains the values of the hyperparameters finally chosen as the best for each method and dataset, listed in Tables A.9 and A.10. DAEF [26], OS-ELM [38], and OC-SVM [39] respectively.
\ • Deep Autoencoder for Federated learning (DAEF)[26].
\ – Architecture: Neurons per layer.
\ – λhid: Regularization hyperparameter of the hidden layer.
\ – λlast: Regularization hyperparameter of the last layer.
\ – µ: Anomaly threshold.
\ • Online Sequential Extreme Learning Machine (OS-ELM)[38]
\ – Architecture: Neurons per layer.
\ – µ: Anomaly threshold.
\ • One-Class Support Vector Machine (OC-SVM)[39].
\ – An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors (ν).
\ – Kernel type: Linear, Polynomial or RBF.
\ – Kernel coefficient γ (in the case of polynomial and RBF kernels).
\ – Degree (in the case of polynomial kernel).
\
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:::info This paper is available on arxiv under CC 4.0 license.
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This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
Writings, Papers and Blogs on Text Models | Sciencx (2024-06-28T14:14:26+00:00) Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix. Retrieved from https://www.scien.cx/2024/06/28/effective-anomaly-detection-pipeline-for-amazon-reviews-references-appendix/
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